fcn demonstrated to be effective for dense per-pixel prediction; however, also their performance degraded because of domain shift. Therefore, some works have been proposed to tackle it. Most of them rely on deep unsupervised da. Zhang et al. [1]} exploited synthetic data to enhance performance in real-world images using the global label distribution loss of the images and local label distribution loss of the landmark super-pixels in the target domain as a regularizer for the fine-tuning of the network. On the other hand, Chen et al. [2]} proposed a framework for cross-city semantic segmentation. [3]} employed a residual network and adversarial training to make the source feature maps closer to the target ones. The authors of [4]} combine semantic segmentation and depth estimation to boost the adaptation performance, providing to the discriminator the segmentation and the depth prediction maps jointly.
